The present disclosure relates to systems and methods for magnetic resonance imaging (“MRI”). More specifically, the disclosure relates high resolution imaging.
Any nucleus that possesses a magnetic moment attempts to align itself with the direction of the magnetic field in which it is located. In doing so, however, the nucleus precesses around this direction at a characteristic angular frequency (Larmor frequency) which is dependent on the strength of the magnetic field and on the properties of the specific nuclear species (the magnetogyric constant γ of the nucleus). Nuclei which exhibit this phenomena are referred to herein as “spins.”
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. A net magnetic moment MZ is produced in the direction of the polarizing field, but the randomly oriented magnetic components in the perpendicular, or transverse, plane (x-y plane) cancel one another. If, however, the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, MZ, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt, which is rotating, or spinning, in the x-y plane at the Larmor frequency. The practical value of this phenomenon resides on signals which are emitted by the excited spins after the pulsed excitation signal B1 is terminated. Depending upon of biologically variable parameters such as proton density, longitudinal relaxation time (“T1”) describing the recovery of MZ along the polarizing field, and transverse relaxation time (“T2”) describing the decay of Mt in the x-y plane, this nuclear magnetic resonance (“NMR”) phenomena is exploited to obtain image contrast using different measurement sequences and by changing imaging parameters.
When utilizing NMR to produce images, a technique is employed to obtain NMR signals from specific locations in the subject. Typically, the region to be imaged (region of interest) is scanned using a sequence of NMR measurement cycles that vary according to the particular localization method being used. To perform such a scan, it is, of course, necessary to elicit NMR signals from specific locations in the subject. This is accomplished by employing magnetic fields (Gx, Gy, and Gz) which have the same direction as the polarizing field B0, but which have a gradient along the respective x, y and z axes. By controlling the strength of these gradients during each NMR cycle, the spatial distribution of spin excitation can be controlled and the location of the resulting NMR signals can be identified. The acquisition of the NMR signals samples is referred to as sampling k-space, and a scan is completed when enough NMR cycles are performed to fully sample k-space. The resulting set of received NMR signals are digitized and processed to reconstruct the image using various reconstruction techniques.
Diffusion-weighted imaging (“DWI”) is an important MRI technique that is based on the measurement of random motion of water molecules in tissues. DWI has been utilized for studying the anatomy of the brain, such as neural architecture and brain connectivity, as well as various brain disorders, including Alzheimer's disease, schizophrenia, mild traumatic brain injury, and so forth. For many clinical applications, such as neurosurgical planning and deep brain stimulation, diffusion images with high spatial resolution are critical for accurately characterizing brain structures. In particular, high resolution is desirable for identifying structures that are very small, such as the substantia nigra and sub-thalamic nucleus, or brain matter structures in neonate and infant brains. In addition, high resolution is advantageous for tracing small white-matter fiber bundles and reducing partial volume effects. However, common voxel sizes of standard diffusion magnetic resonance imaging (“dMRI”) are generally about 23 mm3, which is too large for characterizing brain structures that are a few millimeters thick. Although reductions in voxel size may be possible by modifying acquisition, this would lead to proportionate decreases in the signal-to-noise ratio (“SNR”) due to signal loss from T2 decay, as well as distortions from magnetic field inhomogeneities. In addition, offsetting SNR losses by averaging multiple acquisitions would result acquisition times that would be too long to be practical in a clinical setting. For example, reducing voxel size from 2×2×2 mm3 requires 64 averages to obtain an equivalent SNR.
Existing techniques attempting to obtain high-resolution (“HR”) imaging can be classified into two categories based on their data acquisition scheme. In the first category, HR data is obtained from a single low-resolution (“LR”) image using intelligent interpolations or regularization techniques. In particular, this technique has been applied to natural images, and more recently structural and diffusion MRI. Although successful in preserving and enhancing certain anatomical details, performance of this approach is limited by the information and resolution of the original LR image. In the second category, multiple LR images acquired according to a specific sampling scheme are used to reconstruct a HR image. Each LR image is modeled as the measurement of an underlying HR image via a down-sampling operator such that the observation model relates the original HR image to the observed LR images. Using concepts of super-resolution reconstruction (“SRR”), the HR image is then estimated by solving a linear inverse problem. This approach applied to dMRI typically reconstructs each diffusion-weighted volume of interest independently.
In another approach, an acquisition scheme where the LR images included orthogonal slice acquisition directions was also utilized. However, the distortions from LR scans with different slice directions requires corrections involving a complex non-linear spatial normalization process prior to the application of the SRR algorithm. In addition, each DWI volume was reconstructed independently, requiring the LR images to be acquired or interpolated on the same dense set of gradient directions. Moreover, this method required the same number of measurements (e.g., 60 gradient directions) for each LR acquisition. To address this problem, one recent approach utilizing the diffusion tensor imaging (“DTI”) technique was introduced to model the diffusion signal in q-space. However, a very simplistic diffusion tensor model was assumed, which is often not appropriate for modeling more complex diffusion phenomena, such as white matter fiber crossings.
In light of the above, there is a continued need for improved imaging systems and methods, and in particular for systems and methods capable of high resolution imaging.